This section provides background information related to the present disclosure, which is not necessarily prior art.
Although systems exist for automated or self-driving vehicles, such systems can have difficulty with decision making in complicated or uncertain situations involving dynamic objects, such as other traffic participants, pedestrians, etc. For example, automated or self-driving vehicles can exhibit seemingly unnatural behavior in certain complicated or uncertain situations involving dynamic objects.
Further, conventional planning for an automated or self-driving vehicle generally includes a route planner and a trajectory planner. The route planner determines a global route plan to a destination and the trajectory planner generates an optimal vehicle path for a short time period, such as several seconds, for following the global route plan. Direct optimization of a vehicle trajectory based on a global route plan with consideration for situational awareness (or naturality) over even a few seconds of trajectory can be difficult to perform due to multiple uncertain factors, such as a surrounding vehicle accelerating rapidly, a pedestrian moving into the vehicle's path, etc. For example, when an obstacle or an uncertain environment is introduced, the traditional trajectory planner may attempt to construct an alternative short-term path. In particularly complicated driving areas with high uncertainty and dynamic objects, however, the trajectory planner may need to frequently and iteratively construct alternative paths and, in such case, the motion planning may become intractable and/or time consuming. Furthermore, purely planning a short term path may lead to collisions since it does not consider uncertainty in the intentions of dynamic obstacles and also does not plan to take actions that disambiguate uncertainty, such as honking to attract the attention of a pedestrian crossing over without looking.